Building a Modern and Effective Data Infrastructure
Firms are actively modernizing their data infrastructure, yet many navigate persistent challenges during this transition. The most cited difficulties include security and compliance risks during data migration (52%), the complexity and cost of migrating large data volumes (40%), and minimizing disruption to trading operations (40%). These concerns are compounded by underestimated resource requirements and employee resistance to change.
Despite these hurdles, most firms are progressing in cloud adoption:
- 59% report that over half of their systems are cloud-based.
- 34% have reached 76–100% cloud usage.
- Reflecting this shift, 72% are open to outsourcing data modernization to managed service providers (MSPs).
Firms are leveraging MSPs to accelerate digital transformation, improve compliance oversight, reduce reconciliation dependencies, and scale operations more efficiently. Strategic objectives also include AI/automation enablement, 24/7 monitoring, and enhanced business continuity.
Looking ahead, priorities for data infrastructure enhancement centre on advanced analytics (55%), modernization of storage and processing (54%), and cloud solutions for cost-effectiveness and agility (54%). Transparency, auditability, and unifying disparate data sources also ranked highly. While only 8% prioritized agility to meet evolving needs, it’s clear that foundational upgrades are seen as essential for future-readiness in a rapidly evolving market environment. In particular, many respondents commented on ensuring the data used in AI models is known to be complete, accurate and timely.
“All very well embracing AI — provided it’s not confidently wrong on rubbish data. After all, speed and precision are of little use if you’re racing in the wrong direction. Otherwise, you’re just automating the mistakes.”
Neil Vernon, Chief Product Officer, Gresham
Question 1: What are the biggest challenges your organization has faced when transitioning away from legacy data systems?
(Respondents were asked to select 3 options)
Security risks and compliance concerns associated with data migration
The complexity and cost of migrating large volumes of data
Ensuring minimal disruption to ongoing trading operations and critical processes
Underestimating the time and resources required for the transition
Resistance to change from employees and the need for extensive retraining
Maintaining data quality and accuracy during the migration process
Data integration and ensuring compatibility between old and new systems
Lack of in-house expertise and the need for external consultants
Difficulty in aligning the new data infrastructure with evolving business needs and future scalability
Question 2: What percentage of your systems are currently cloud-based?
51-75%
76-100%
26-50%
"These results don’t surprise me — legacy systems and platforms are still widely used across the industry. Transitioning to a SaaS model is both time-consuming and costly. You need the right resources, capabilities, and sufficient time to execute the move effectively. From my perspective, this isn’t a matter of choice anymore; it’s a necessity and simply the direction we’re heading.
To manage both technology and operations effectively, the ability to upgrade without interruption is critical. In the cloud, this becomes far more achievable. I recall it used to take us several months to prepare for the shift, followed by a full weekend dedicated to updates and upgrades of the on-prem application. With traditional systems, this kind of change can be cumbersome, whereas in the cloud it happens seamlessly in the background. That’s why I believe this shift will only continue to accelerate."
Krzysztof Wierzchowski, SVP Business Transformation, Franklin Templeton
Question 3: Would your firm consider outsourcing your data modernization projects to a managed service provider?
Yes
No
"Cloud adoption is no longer a question of ‘if,’ but ‘how well when we do it.’ The firms making the most progress are those addressing migration risks head-on, leveraging managed service providers so they can run their businesses, and focusing on the quality and completeness of the data that underpins their AI strategies."
Matthew Greninger, General Manager, Data Automation Solutions, Gresham
"We’ve been actively implementing an outsourcing program across various functions where we felt we could benefit from external expertise. From the company’s perspective, we’ve been able to identify partners capable of matching our global footprint and operational complexity — delivering services at the right quality level in areas that aren’t our core competitive advantage.
For example, we outsourced our transfer agent function to GPM via FIS, and similarly, we’ve outsourced fund accounting to GPM via JPM. These partners bring the infrastructure and scale we need across geographies, enabling them to deliver the services at the standard and efficiency we require to support our strategy."
Krzysztof Wierzchowski, SVP Business Transformation, Franklin Templeton
Question 5: What are your firm's priorities as you look to enhance your data infrastructure?
(Respondents were asked to select all that apply)
Implementing advanced analytics and AI/ML capabilities for predictive insights
Modernizing data storage and processing capabilities for greater efficiency and scalability
Adopting cloud-based data solutions for flexibility and cost-effectiveness
Improving data lineage and auditability for better transparency and control
Integrating disparate data sources to create a unified view of information
Enhancing data quality and reliability for more accurate insights
Strengthening data privacy measures and compliance with data protection regulations
Improving data security and governance to meet regulatory requirements
Developing a more agile and responsive data infrastructure to support evolving business needs
"I wasn’t surprised by the results — the range of responses reflects the diversity of company sizes, AUM levels, and operational maturity across the industry. The challenges firms face vary significantly depending on those factors. What stood out to me is that artificial intelligence emerged as a top priority. That aligns with what we’re seeing across the board; this isn’t just a passing trend — it’s a meaningful industry shift.
AI is helping us on multiple fronts. On one hand, it simplifies and streamlines well-defined tasks, reducing manual effort and costs. On the other, it plays a vital role in more advanced applications like predictive analytics. With access to large data pools, we’re able to build models that guide decision-making in key areas. I expect we’ll see even more use cases emerge in the months ahead. Internally, we’re focused on monetizing our tech stack, consolidating data, and refining our tech and ops models post-integration. The goal is a scalable, standardized model that meets diverse needs while also controlling costs across a large and complex organization."
Krzysztof Wierzchowski, SVP Business Transformation, Franklin Templeton
